Optimum Detector for Spatial Modulation using Sparsity Recovery in Compressive Sensing

Objective: Spatial Modulation (SM) is proposed for next generation green communications due its high spectral and energy efficiency. SM is incorporated with massive MIMO structures to leverage its high potential in the application for future generation wireless networks. Methods/statistical Analysis: The optimal ML detector for SM-MIMO systems requires enormous computational complexity, which makes the implementation infeasible in practice. However, the greedy low complexity detectors suffer from inferior performance and have huge performance gap from the optimal detectors. In this paper we propose new transmission schemes and detector structures for SM-MIMO systems. Findings: In particular the transmitter imposes certain structures, known as joint sparse, and the receiver exploits the information in detecting the symbols. We have shown that our proposed detector performs better than other greedy algorithms in the literature and performs close to the ML solution. We establish theoretical recovery guarantees for our proposed approach and compare the performance in theoretical and simulation results. Improvements: The theoretical characterization shows significant improvement in the detection performance compared to the conventional schemes. It is shown in simulation that the proposed algorithm achieves a gain of 4 dB compared to the conventional detectors.